Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	19.224901
daal4py_KNeighborsClassifier	0.0	3.0	14.974038
KNeighborsClassifier_kd_tree	0.0	7.0	2.729862
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	43.911961
KMeans_tall	0.0	1.0	39.927861
daal4py_KMeans_tall	0.0	1.0	14.283303
KMeans_short	0.0	0.0	20.823591
daal4py_KMeans_short	0.0	0.0	8.978808
LogisticRegression	0.0	0.0	59.618679
daal4py_LogisticRegression	0.0	0.0	52.859150
Ridge	0.0	0.0	45.974159
daal4py_Ridge	0.0	0.0	14.951763
total	0.0	31.0	38.321814

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.126	0.001	1000000	1000000	100	brute	-1	1	0.982	0.982	0.442	0.001	0.284	0.002	See
1	KNeighborsClassifier	predict	24.472	0.517	1000000	1000	100	brute	-1	1	0.982	0.982	1.924	0.016	12.720	0.288	See
2	KNeighborsClassifier	predict	0.157	0.013	1000000	1	100	brute	-1	1	0.982	0.982	0.083	0.001	1.902	0.161	See
3	KNeighborsClassifier	fit	0.130	0.002	1000000	1000000	100	brute	-1	5	0.982	0.982	0.452	0.005	0.288	0.006	See
4	KNeighborsClassifier	predict	31.676	0.000	1000000	1000	100	brute	-1	5	0.982	0.982	1.926	0.021	16.446	0.176	See
5	KNeighborsClassifier	predict	0.161	0.011	1000000	1	100	brute	-1	5	0.982	0.982	0.082	0.000	1.961	0.129	See
6	KNeighborsClassifier	fit	0.129	0.002	1000000	1000000	100	brute	-1	100	0.982	0.982	0.439	0.001	0.293	0.005	See
7	KNeighborsClassifier	predict	31.530	0.000	1000000	1000	100	brute	-1	100	0.982	0.982	1.999	0.015	15.773	0.115	See
8	KNeighborsClassifier	predict	0.171	0.011	1000000	1	100	brute	-1	100	0.982	0.982	0.082	0.000	2.077	0.139	See
9	KNeighborsClassifier	fit	0.131	0.003	1000000	1000000	100	brute	1	1	0.982	0.982	0.454	0.005	0.288	0.007	See
10	KNeighborsClassifier	predict	12.127	0.017	1000000	1000	100	brute	1	1	0.982	0.982	1.912	0.012	6.342	0.039	See
11	KNeighborsClassifier	predict	0.176	0.004	1000000	1	100	brute	1	1	0.982	0.982	0.084	0.005	2.101	0.139	See
12	KNeighborsClassifier	fit	0.130	0.003	1000000	1000000	100	brute	1	5	0.982	0.982	0.441	0.001	0.295	0.006	See
13	KNeighborsClassifier	predict	20.162	0.028	1000000	1000	100	brute	1	5	0.982	0.982	1.885	0.017	10.698	0.100	See
14	KNeighborsClassifier	predict	0.185	0.001	1000000	1	100	brute	1	5	0.982	0.982	0.082	0.001	2.262	0.039	See
15	KNeighborsClassifier	fit	0.129	0.002	1000000	1000000	100	brute	1	100	0.982	0.982	0.449	0.004	0.288	0.006	See
16	KNeighborsClassifier	predict	20.299	0.010	1000000	1000	100	brute	1	100	0.982	0.982	1.969	0.019	10.311	0.101	See
17	KNeighborsClassifier	predict	0.189	0.003	1000000	1	100	brute	1	100	0.982	0.982	0.083	0.001	2.280	0.038	See
18	KNeighborsClassifier	fit	0.056	0.001	1000000	1000000	2	brute	-1	1	0.982	0.982	0.090	0.004	0.625	0.025	See
19	KNeighborsClassifier	predict	20.326	0.105	1000000	1000	2	brute	-1	1	0.982	0.982	0.300	0.003	67.755	0.758	See
20	KNeighborsClassifier	predict	0.020	0.003	1000000	1	2	brute	-1	1	0.982	0.982	0.006	0.000	3.346	0.439	See
21	KNeighborsClassifier	fit	0.056	0.000	1000000	1000000	2	brute	-1	5	0.982	0.982	0.088	0.002	0.633	0.018	See
22	KNeighborsClassifier	predict	28.051	0.015	1000000	1000	2	brute	-1	5	0.982	0.982	0.302	0.002	92.860	0.586	See
23	KNeighborsClassifier	predict	0.032	0.002	1000000	1	2	brute	-1	5	0.982	0.982	0.006	0.000	5.288	0.354	See
24	KNeighborsClassifier	fit	0.051	0.000	1000000	1000000	2	brute	-1	100	0.982	0.982	0.089	0.001	0.577	0.006	See
25	KNeighborsClassifier	predict	27.858	0.152	1000000	1000	2	brute	-1	100	0.982	0.982	0.357	0.002	78.139	0.613	See
26	KNeighborsClassifier	predict	0.033	0.002	1000000	1	2	brute	-1	100	0.982	0.982	0.007	0.001	4.858	0.660	See
27	KNeighborsClassifier	fit	0.066	0.003	1000000	1000000	2	brute	1	1	0.982	0.982	0.089	0.003	0.733	0.038	See
28	KNeighborsClassifier	predict	9.511	0.018	1000000	1000	2	brute	1	1	0.982	0.982	0.302	0.004	31.474	0.388	See
29	KNeighborsClassifier	predict	0.015	0.001	1000000	1	2	brute	1	1	0.982	0.982	0.006	0.000	2.384	0.190	See
30	KNeighborsClassifier	fit	0.065	0.002	1000000	1000000	2	brute	1	5	0.982	0.982	0.089	0.001	0.730	0.028	See
31	KNeighborsClassifier	predict	17.827	0.012	1000000	1000	2	brute	1	5	0.982	0.982	0.303	0.004	58.894	0.795	See
32	KNeighborsClassifier	predict	0.027	0.001	1000000	1	2	brute	1	5	0.982	0.982	0.006	0.000	4.674	0.211	See
33	KNeighborsClassifier	fit	0.066	0.002	1000000	1000000	2	brute	1	100	0.982	0.982	0.089	0.003	0.740	0.037	See
34	KNeighborsClassifier	predict	16.709	0.011	1000000	1000	2	brute	1	100	0.982	0.982	0.360	0.001	46.464	0.195	See
35	KNeighborsClassifier	predict	0.025	0.001	1000000	1	2	brute	1	100	0.982	0.982	0.006	0.000	4.167	0.241	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	3.231	0.042	1000000	1000000	10	kd_tree	-1	1	0.987	0.984	0.708	0.014	4.567	0.110	See
1	KNeighborsClassifier_kd_tree	predict	0.459	0.004	1000000	1000	10	kd_tree	-1	1	0.987	0.984	0.107	0.002	4.273	0.071	See
2	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	-1	1	0.987	0.984	0.000	0.000	8.842	4.038	See
3	KNeighborsClassifier_kd_tree	fit	3.212	0.039	1000000	1000000	10	kd_tree	-1	5	0.987	0.984	0.740	0.021	4.340	0.133	See
4	KNeighborsClassifier_kd_tree	predict	0.869	0.007	1000000	1000	10	kd_tree	-1	5	0.987	0.984	0.195	0.003	4.464	0.076	See
5	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	-1	5	0.987	0.984	0.000	0.000	6.031	2.212	See
6	KNeighborsClassifier_kd_tree	fit	3.186	0.043	1000000	1000000	10	kd_tree	-1	100	0.987	0.984	0.701	0.009	4.546	0.083	See
7	KNeighborsClassifier_kd_tree	predict	2.750	0.018	1000000	1000	10	kd_tree	-1	100	0.987	0.984	0.576	0.005	4.776	0.053	See
8	KNeighborsClassifier_kd_tree	predict	0.004	0.000	1000000	1	10	kd_tree	-1	100	0.987	0.984	0.001	0.000	2.956	0.778	See
9	KNeighborsClassifier_kd_tree	fit	3.257	0.033	1000000	1000000	10	kd_tree	1	1	0.987	0.984	0.745	0.018	4.372	0.115	See
10	KNeighborsClassifier_kd_tree	predict	0.743	0.003	1000000	1000	10	kd_tree	1	1	0.987	0.984	0.106	0.001	7.045	0.102	See
11	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	0.987	0.984	0.000	0.000	2.604	1.222	See
12	KNeighborsClassifier_kd_tree	fit	3.290	0.039	1000000	1000000	10	kd_tree	1	5	0.987	0.984	0.699	0.007	4.705	0.072	See
13	KNeighborsClassifier_kd_tree	predict	1.446	0.002	1000000	1000	10	kd_tree	1	5	0.987	0.984	0.186	0.002	7.783	0.088	See
14	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	5	0.987	0.984	0.000	0.000	2.199	1.046	See
15	KNeighborsClassifier_kd_tree	fit	3.258	0.031	1000000	1000000	10	kd_tree	1	100	0.987	0.984	0.737	0.021	4.417	0.131	See
16	KNeighborsClassifier_kd_tree	predict	4.712	0.033	1000000	1000	10	kd_tree	1	100	0.987	0.984	0.578	0.014	8.155	0.210	See
17	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	1	100	0.987	0.984	0.001	0.000	1.435	0.541	See
18	KNeighborsClassifier_kd_tree	fit	1.318	0.049	1000000	1000000	2	kd_tree	-1	1	0.987	0.984	0.498	0.016	2.648	0.131	See
19	KNeighborsClassifier_kd_tree	predict	0.027	0.002	1000000	1000	2	kd_tree	-1	1	0.987	0.984	0.001	0.000	37.528	16.736	See
20	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	1	0.987	0.984	0.000	0.000	21.368	17.747	See
21	KNeighborsClassifier_kd_tree	fit	1.278	0.019	1000000	1000000	2	kd_tree	-1	5	0.987	0.984	0.494	0.018	2.586	0.103	See
22	KNeighborsClassifier_kd_tree	predict	0.026	0.000	1000000	1000	2	kd_tree	-1	5	0.987	0.984	0.001	0.000	24.233	8.467	See
23	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	5	0.987	0.984	0.000	0.000	19.470	15.038	See
24	KNeighborsClassifier_kd_tree	fit	1.313	0.025	1000000	1000000	2	kd_tree	-1	100	0.987	0.984	0.504	0.017	2.607	0.101	See
25	KNeighborsClassifier_kd_tree	predict	0.048	0.002	1000000	1000	2	kd_tree	-1	100	0.987	0.984	0.007	0.000	7.151	0.474	See
26	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	100	0.987	0.984	0.000	0.000	19.184	14.440	See
27	KNeighborsClassifier_kd_tree	fit	1.264	0.031	1000000	1000000	2	kd_tree	1	1	0.987	0.984	0.497	0.016	2.545	0.105	See
28	KNeighborsClassifier_kd_tree	predict	0.023	0.000	1000000	1000	2	kd_tree	1	1	0.987	0.984	0.001	0.000	30.698	13.613	See
29	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	0.987	0.984	0.000	0.000	5.669	4.398	See
30	KNeighborsClassifier_kd_tree	fit	1.280	0.021	1000000	1000000	2	kd_tree	1	5	0.987	0.984	0.495	0.018	2.588	0.105	See
31	KNeighborsClassifier_kd_tree	predict	0.031	0.007	1000000	1000	2	kd_tree	1	5	0.987	0.984	0.001	0.000	27.904	10.966	See
32	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	0.987	0.984	0.000	0.000	5.729	4.670	See
33	KNeighborsClassifier_kd_tree	fit	1.300	0.046	1000000	1000000	2	kd_tree	1	100	0.987	0.984	0.492	0.016	2.644	0.126	See
34	KNeighborsClassifier_kd_tree	predict	0.054	0.001	1000000	1000	2	kd_tree	1	100	0.987	0.984	0.008	0.002	7.021	1.859	See
35	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	0.987	0.984	0.000	0.000	5.568	4.629	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.564	0.007	1000000	1000000	2	full	k-means++	30	3	1	0.0	30	0.002	30	0.001	0.466	0.040	1.210	0.105	See
1	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	30	0.002	30	0.001	0.000	0.000	0.967	0.561	See
2	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	30	0.002	30	0.001	0.000	0.000	2.343	2.122	See
3	KMeans_tall	fit	0.491	0.003	1000000	1000000	2	full	random	30	3	1	0.0	30	0.002	30	0.001	0.418	0.025	1.176	0.071	See
4	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	30	0.002	30	0.001	0.000	0.000	1.948	1.407	See
5	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	30	0.002	30	0.001	0.000	0.000	2.405	2.001	See
6	KMeans_tall	fit	6.270	0.024	1000000	1000000	100	full	k-means++	30	3	1	0.0	30	0.002	30	0.001	2.901	0.010	2.161	0.011	See
7	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	30	0.002	30	0.001	0.000	0.000	2.056	1.451	See
8	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	30	0.002	30	0.001	0.000	0.000	1.447	1.102	See
9	KMeans_tall	fit	5.783	0.013	1000000	1000000	100	full	random	30	3	1	0.0	30	0.002	30	0.001	2.755	0.014	2.099	0.012	See
10	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	30	0.002	30	0.001	0.000	0.000	2.115	1.519	See
11	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	30	0.002	30	0.001	0.000	0.000	1.710	1.397	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.324	0.015	10000	10000	2	full	k-means++	30	300	1	0.0	21	0.001	30	0.005	0.101	0.002	3.208	0.158	See
1	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	21	0.001	30	0.005	0.001	0.000	1.020	0.234	See
2	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	21	0.001	30	0.005	0.000	0.000	1.675	1.255	See
3	KMeans_short	fit	0.126	0.001	10000	10000	2	full	random	30	300	1	0.0	30	0.001	30	0.005	0.046	0.001	2.709	0.048	See
4	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	30	0.001	30	0.005	0.001	0.000	1.066	0.257	See
5	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	30	0.001	30	0.005	0.000	0.000	1.971	1.571	See
6	KMeans_short	fit	0.795	0.028	10000	10000	100	full	k-means++	30	300	1	0.0	23	0.001	20	0.005	0.361	0.025	2.205	0.172	See
7	KMeans_short	predict	0.002	0.001	10000	1000	100	full	k-means++	30	300	1	0.0	23	0.001	20	0.005	0.001	0.000	1.943	1.124	See
8	KMeans_short	predict	0.000	0.000	10000	1	100	full	k-means++	30	300	1	0.0	23	0.001	20	0.005	0.000	0.000	1.906	1.047	See
9	KMeans_short	fit	0.266	0.051	10000	10000	100	full	random	30	300	1	0.0	30	0.001	20	0.005	0.173	0.024	1.541	0.363	See
10	KMeans_short	predict	0.004	0.002	10000	1000	100	full	random	30	300	1	0.0	30	0.001	20	0.005	0.001	0.000	3.423	1.377	See
11	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	30	0.001	20	0.005	0.000	0.000	1.906	1.168	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	10.655	0.024	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	0.27	10.635	0.015	1.002	0.003	See
1	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	0.27	0.000	0.000	0.765	0.371	See
2	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	0.27	0.000	0.000	0.353	0.346	See
3	LogisticRegression	fit	0.753	0.017	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[27]	0.27	0.733	0.026	1.028	0.044	See
4	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[27]	0.27	0.003	0.000	0.584	0.095	See
5	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[27]	0.27	0.001	0.000	0.123	0.090	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	1.726	0.056	100000	100000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.910	0.011	1.897	0.066	See
1	Ridge	predict	0.001	0.000	100000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.002	0.001	0.521	0.357	See
2	Ridge	predict	0.000	0.000	100000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.000	0.000	0.669	0.747	See
3	Ridge	fit	1.079	0.010	1000000	1000000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.230	0.010	4.696	0.213	See
4	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.000	0.000	0.689	0.473	See
5	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.000	0.000	0.651	0.740	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.2",
    "numpy": "1.20.2",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}